Effect of additional tempering temperature on elastic properties of steel 65G

The remarkable deformability of the erythrocyte derives in large part from the elastic properties of spectrin, the major component of the membrane skeleton. It is generally accepted that spectrin's elasticity arises from marked conformational changes which include variations in its overall length (1). In this work the structure of spectrin in partially expanded membrane skeletons was studied by electron microscopy to determine the molecular basis for spectrin's elastic properties. Spectrin molecules were analysed with respect to three features: length, conformation, and quaternary structure. The results of these studies lead to a model of how spectrin mediates the elastic deformation of the erythrocyte.Membrane skeletons were isolated from erythrocyte membrane ghosts, negatively stained, and examined by transmission electron microscopy (2). Particle lengths and end-to-end distances were measured from enlarged prints using the computer program MACMEASURE. Spectrin conformation (straightness) was assessed by calculating the particles’ correlation length by iterative approximation (3). Digitised spectrin images were correlation averaged or Fourier filtered to improve their signal-to-noise ratios. Three-dimensional reconstructions were performed using a suite of programs which were based on the filtered back-projection algorithm and executed on a cluster of Microvax 3200 workstations (4).

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The buckling of thin EM specimens

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100177386 ◽

1990 ◽

Vol 48 (4) ◽

pp. 850-851

Author(s):

A.R. Thölén

Keyword(s):

Electron Microscope ◽

Elastic Properties ◽

Crystal Surface ◽

Specimen Surface ◽

Radius Of Curvature ◽

Thin Foils ◽

Thin Electron ◽

Regular Patterns

Thin electron microscope specimens often contain irregular bend contours (Figs. 1-3). Very regular bend patterns have, however, been observed around holes in some ion-milled specimens. The purpose of this investigation is twofold. Firstly, to find the geometry of bent specimens and the elastic properties of extremely thin foils and secondly, to obtain more information about the background to the observed regular patterns.The specimen surface is described by z = f(x,y,p), where p is a parameter, eg. the radius of curvature of a sphere. The beam is entering along the z—direction, which coincides with the foil normal, FN, of the undisturbed crystal surface (z = 0). We have here used FN = [001]. Furthermore some low indexed reflections are chosen around the pole FN and in our fcc crystal the following g-vectors are selected:

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The Hydrostatic Pressure Effect on Elastic Properties of B2-Phase Single Crystals Ti50Ni48Fe2and Ti50Ni45Fe5Alloys

Journal de Physique IV (Proceedings) ◽

10.1051/jp4/199558729 ◽

1995 ◽

Vol 05 (C8) ◽

pp. C8-729-C8-734

Author(s):

A.I. Lotkov ◽

V.P. Lapshin ◽

V.A. Goncharova ◽

H.V Chernysheva ◽

V.N. Grishkov ◽

...

Keyword(s):

Hydrostatic Pressure ◽

Single Crystals ◽

Elastic Properties ◽

Pressure Effect ◽

B2 Phase

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Elastic properties of solids at high pressure

Uspekhi Fizicheskih Nauk ◽

10.3367/ufnr.0185.201511f.1215 ◽

2015 ◽

Vol 185 (11) ◽

pp. 1215-1224 ◽

Cited By ~ 2

Author(s):

Yurii Kh. Vekilov ◽

Oleg M. Krasil'nikov ◽

Andrei V. Lugovskoy

Keyword(s):

High Pressure ◽

Elastic Properties

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Wear volume prediction of AISI H13 die steel using response surface methodology and artificial neural network

JOURNAL OF MECHANICAL ENGINEERING AND SCIENCES ◽

10.15282/jmes.14.2.2020.19.0531 ◽

2020 ◽

Vol 14 (2) ◽

pp. 6789-6800

Author(s):

Vishal Jagota ◽

Rajesh Kumar Sharma

Keyword(s):

Neural Network ◽

Heat Treatment ◽

Artificial Neural Network ◽

Response Surface ◽

Sliding Wear ◽

Austenitizing Temperature ◽

Tempering Temperature ◽

Die Steel ◽

Artificial Neural ◽

Tempering Time

Resistance to wear of hot die steel is dependent on its mechanical properties governed by the microstructure. The required properties for given application of hot die steel can be obtained with control the microstructure by heat treatment parameters. In the present paper impact of different heat treatment parameters like austenitizing temperature, tempering time, tempering temperature is studied using response surface methodology (RSM) and artificial neural network (ANN) to predict sliding wear of H13 hot die steel. After heat treating samples at austenitizing temperature of 1020°C, 1040°C and 1060°C; tempering temperature 540°C, 560°C and 580°C; tempering time 1hour, 2hours and 3hours, experimentation on pin-on-disc tribo-tester is done to measure the sliding wear of H13 die steel. Box-Behnken design is used to develop a regression model and analysis of variance technique is used to verify the adequacy of developed model in case of RSM. Whereas, multi-layer feed-forward backpropagation architecture with input layer, single hidden layer and an output layer is used in ANN. It was found that ANN proves to be a better tool to predict sliding wear with more accuracy. Correlation coefficient R2 of the artificial neural network model is 0.986 compared to R2 of 0.957 for RSM. However, impact of input parameter interactions can only be analysed using response surface method. In addition, sensitivity analysis is done to determine the heat treatment parameter exerting most influence on the wear resistance of H13 hot die steel and it showed that tempering time has maximum influence on wear volume, followed by tempering temperature and austenitizing temperature. The prediction models will help to estimate the variation in die lifetime by finding the amount of wear that will occur during use of hot die steel, if the heat treatment parameters are varied to achieve different properties.

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